Buddy Benchmark

Buddy Benchmark is an extensible benchmark framework. We intend to provide a platform for performance comparison of various frameworks and optimizers. This project is based on Google Benchmark.

Clone the project:

$ git clone [email protected]:buddy-compiler/buddy-benchmark.git

Choose and Build Dependencies

Choose Submodules

$ git submodule update --init

Build OpenCV

$ cd buddy-benchmark/thirdparty/opencv
$ mkdir build && cd build
$ cmake -G Ninja .. -DCMAKE_BUILD_TYPE=Release
$ ninja

Image Processing Benchmark

Currently, the image processing benchmark includes the following frameworks or optimizers:

• OpenCV (link)

NOTE: Please build OpenCV from source to achieve the best performance.

• Eigen (link)

• Buddy MLIR (link)

NOTE: Please make sure the buddy-opt tool of buddy-mlir project can work well.

Run the image processing benchmark:

CMake Options	Default Value
-DBUDDY_OPT_STRIP_MINING	256
-DMLIR_LINALG_TILE	2
-DBUDDY_OPT_ATTR	avx512f
-DBUDDY_OPT_TRIPLE	x86_64-unknown-linux-gnu

Note:

1. Please replace the /PATH/TO/* with your local path.

2. For running executable :

i. Please replace <image path> with path of the image which is to be used for benchmarking.

ii. Please replace <kernel name> with name of the kernel which is to be used for benchmarking as specifed in include/ImageProcessing/Kernels.h.

ii. Please replace <kernelmorph name> with name of the unsigned int kernel which is to be used for benchmarking as specifed in include/ImageProcessing/Kernels.h.

iii. Please replace <Boundary Option> with CONSTANT_PADDING or REPLICATE_PADDING.

Ex. ./image-processing-benchmark ../../benchmarks/ImageProcessing/Images/YuTu.png random3x3KernelAlign random3x3KernelAlignInt CONSTANT_PADDING

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DIMAGE_PROCESSING_BENCHMARKS=ON \
    -DOpenCV_DIR=$PWD/../thirdparty/opencv/build/ \
    -DEIGEN_DIR=$PWD/../thirdparty/eigen/ \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja image-processing-benchmark
$ cd bin && ./image-processing-benchmark <image path> <kernel name> <kernelmorph name> <Boundary Option>

Deep Learning Benchmark

CMake Options	Default Value
-DBUDDY_OPT_ATTR	avx512f
-DBUDDY_OPT_TRIPLE	x86_64-unknown-linux-gnu

Note: Please replace the /PATH/TO/* with your local path.

$ cd buddy-benchmark
$ git lfs pull
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DDEEP_LEARNING_BENCHMARKS=ON \
    -DOpenCV_DIR=$PWD/../thirdparty/opencv/build/ \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja

The deep learning benchmark includes the following e2e models and operations:

• MobileNet

We generated the model code with IREE and made appropriate modifications, and then compiled it with the MLIR tool chain.

Run the MobileNet benchmark:

$ cd <path to build>/bin && ./mobilenet-benchmark

• DepthwiseConv2DNhwcHwc Operation

Run the DepthwiseConv2DNhwcHwc operation benchmark:

$ cd <path to build>/bin && ./depthwise-conv-2d-nhwc-hwc-benchmark

Audio Processing Benchmark

Currently, the audio processing benchmark includes the following frameworks or optimizers:

• KFR (link)

Note: Please replace the /PATH/TO/* with your local path.

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DAUDIO_PROCESSING_BENCHMARKS=ON \
    -DCMAKE_CXX_COMPILER=clang++ \
    -DKFR_DIR=/PATH/TO/KFR/SOURCE/CODE \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja audio-processing-benchmark
$ cd bin
$ ./audio-processing-benchmark

audio-plot tool

To better demonstrate the result after processing, we provide a tool for figure plotting. To use this tool, you have to make sure that you are using python3 and that the numpy, matplotlib and scipy packages have been installed properly. Use the following command to install the required packages:

$ pip install matplotlib scipy

You can customize the python3 path by adding the option -DPYTHON_BINARY_DIR=/PATH/TO/PYTHON/BIN while building:

Note: Please replace the /PATH/TO/* with your local path.

$ cd build
$ cmake -G Ninja .. \
    -DAUDIO_PROCESSING_BENCHMARKS=ON \
    -DCMAKE_CXX_COMPILER=clang++ \
    -DKFR_DIR=/PATH/TO/KFR/SOURCE/CODE \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD \
    -DPYTHON_BINARY_DIR=/PATH/TO/PYTHON/BIN/
$ ninja audio-plot

Once the processing is done, you can use this tool to plot a comparision figure:

$ cd bin
$ ./audio-plot ../../benchmarks/AudioProcessing/Audios/NASA_Mars.wav ResultKFRIir.wav

The result is saved in bin/res.png. For more usage, use audio-plot -h for detailed information.

Vectorization Benchmark

Some of the benchmarks are ported from gcc-loops(link) in LLVM test suit and linpackc(link)

Note: Please replace the /PATH/TO/* with your local path and the XXX with specific target name (ex: gccloops,linpackc,matrix).

$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DVECTORIZATION_BENCHMARKS=ON \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja vectorization-XXX-benchmark
$ cd bin
$ ./vectorization-XXX-benchmark

Gemmini Benchmark

Currently, we use the Spike simulator to run the Gemmini cases. The cycle-accurate benchmark cases are working in the progress. Before building the benchmark target, please see the following table and ensure you use the correct configuration.

Cases	Hardware Configuration
Gemmini-ResNet-101	defaultFpConfig (link)

We assume you have already built all the components in the Gemmini README file. Now, let's build and run the cases.

$ source /path/to/chipyard/env.sh
$ cd buddy-benchmark
$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/ \
    -DGEMMINI_BENCHMARKS=ON
$ ninja
$ cd bin
$ spike --extension=gemmini pk Gemmini-ResNet-101

Operation Optimization Benchmark

Build and run MLIR operation optimization benchmark cases.

$ mkdir build && cd build
$ cmake -G Ninja .. \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DOP_OPTIMIZATION_BENCHMARKS=ON \
    -DBUDDY_MLIR_BUILD_DIR=/PATH/TO/BUDDY-MLIR/BUILD/
$ ninja <your target operation benchmark>

// Operation benchamrk supported include:
//   - conv2d-nchw-fchw-benchmark
//   - matmul-benchmark

Run TVM operation optimization benchmark cases.

• Install TVM (steps).
• Enter to your TVM (virtual) environment.
• Configure TVM path and Python path.
• Navigate to your target operation directory (e.g. buddy-benchmark/benchmarks/OpOptimization/MatMul/TVM).
• (Optional) Configure the main file to specify the target or size of the benchmark.
• Run the main python file.

(tvm)$ export TVM_HOME=/path/to/tvm
(tvm)$ export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}
(tvm)$ cd benchmarks/OpOptimization/<target operation>/TVM
(tvm)$ python main.py